How to apply scipy.signal.filtfilt() on incomplete data
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0
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I want to plot incomplete data (some values are None). In addition I want to apply a butter function on the dataset and show both graphs, incomplete and smoothened. The filter function seems to not work with incomplete data.
Data File: data.csv
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import signal
data = np.genfromtxt('data.csv', delimiter = ',')
df = pd.DataFrame(data)
df.set_index(0, inplace = True)
b, a = signal.butter(5, 0.1)
y = signal.filtfilt(b,a, df[1].values)
df2 = pd.DataFrame(y, index=df.index)
df.plot()
df2.plot()
plt.show()
python pandas numpy matplotlib scipy
asked Nov 12 at 12:16
Artur Müller Romanov
367212
add a comment |
up vote
0
down vote
favorite
I want to plot incomplete data (some values are None). In addition I want to apply a butter function on the dataset and show both graphs, incomplete and smoothened. The filter function seems to not work with incomplete data.
Data File: data.csv
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import signal
data = np.genfromtxt('data.csv', delimiter = ',')
df = pd.DataFrame(data)
df.set_index(0, inplace = True)
b, a = signal.butter(5, 0.1)
y = signal.filtfilt(b,a, df[1].values)
df2 = pd.DataFrame(y, index=df.index)
df.plot()
df2.plot()
plt.show()
python pandas numpy matplotlib scipy
asked Nov 12 at 12:16
Artur Müller Romanov
367212
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I want to plot incomplete data (some values are None). In addition I want to apply a butter function on the dataset and show both graphs, incomplete and smoothened. The filter function seems to not work with incomplete data.
Data File: data.csv
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import signal
data = np.genfromtxt('data.csv', delimiter = ',')
df = pd.DataFrame(data)
df.set_index(0, inplace = True)
b, a = signal.butter(5, 0.1)
y = signal.filtfilt(b,a, df[1].values)
df2 = pd.DataFrame(y, index=df.index)
df.plot()
df2.plot()
plt.show()
python pandas numpy matplotlib scipy
asked Nov 12 at 12:16
Artur Müller Romanov
367212
I want to plot incomplete data (some values are None). In addition I want to apply a butter function on the dataset and show both graphs, incomplete and smoothened. The filter function seems to not work with incomplete data.
Data File: data.csv
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import signal
data = np.genfromtxt('data.csv', delimiter = ',')
df = pd.DataFrame(data)
df.set_index(0, inplace = True)
b, a = signal.butter(5, 0.1)
y = signal.filtfilt(b,a, df[1].values)
df2 = pd.DataFrame(y, index=df.index)
df.plot()
df2.plot()
plt.show()
python pandas numpy matplotlib scipy
python pandas numpy matplotlib scipy
asked Nov 12 at 12:16
Artur Müller Romanov
367212
asked Nov 12 at 12:16
Artur Müller Romanov
367212
asked Nov 12 at 12:16
Artur Müller Romanov
367212
asked Nov 12 at 12:16
Artur Müller Romanov
367212
asked Nov 12 at 12:16
Artur Müller Romanov
367212
367212
add a comment |
add a comment |
1 Answer
1
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oldest
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up vote
1
down vote
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The documentation page does not mention anything related to NaN. You may have to first remove the NaN from your list of values. Here is a way to do it using Numpy isnan function:
y = signal.filtfilt(b, a, df[1].values[~np.isnan(df[1].values)])
df2 = pd.DataFrame(y, index=df.index[~np.isnan(df[1].values)])
answered Nov 12 at 12:33
Patol75
6136
1
That might be a reasonable solution. But the filter functions inscipy.signalassume the data is sampled at regular intervals. If you discard thenanvalues, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.
– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
The documentation page does not mention anything related to NaN. You may have to first remove the NaN from your list of values. Here is a way to do it using Numpy isnan function:
y = signal.filtfilt(b, a, df[1].values[~np.isnan(df[1].values)])
df2 = pd.DataFrame(y, index=df.index[~np.isnan(df[1].values)])
answered Nov 12 at 12:33
Patol75
6136
1
That might be a reasonable solution. But the filter functions inscipy.signalassume the data is sampled at regular intervals. If you discard thenanvalues, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.
– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
add a comment |
up vote
1
down vote
accepted
The documentation page does not mention anything related to NaN. You may have to first remove the NaN from your list of values. Here is a way to do it using Numpy isnan function:
y = signal.filtfilt(b, a, df[1].values[~np.isnan(df[1].values)])
df2 = pd.DataFrame(y, index=df.index[~np.isnan(df[1].values)])
answered Nov 12 at 12:33
Patol75
6136
1
That might be a reasonable solution. But the filter functions inscipy.signalassume the data is sampled at regular intervals. If you discard thenanvalues, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.
– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
The documentation page does not mention anything related to NaN. You may have to first remove the NaN from your list of values. Here is a way to do it using Numpy isnan function:
y = signal.filtfilt(b, a, df[1].values[~np.isnan(df[1].values)])
df2 = pd.DataFrame(y, index=df.index[~np.isnan(df[1].values)])
answered Nov 12 at 12:33
Patol75
6136
The documentation page does not mention anything related to NaN. You may have to first remove the NaN from your list of values. Here is a way to do it using Numpy isnan function:
y = signal.filtfilt(b, a, df[1].values[~np.isnan(df[1].values)])
df2 = pd.DataFrame(y, index=df.index[~np.isnan(df[1].values)])
answered Nov 12 at 12:33
Patol75
6136
edited Nov 12 at 12:54
answered Nov 12 at 12:33
Patol75
6136
answered Nov 12 at 12:33
Patol75
6136
answered Nov 12 at 12:33
Patol75
6136
6136
1
That might be a reasonable solution. But the filter functions inscipy.signalassume the data is sampled at regular intervals. If you discard thenanvalues, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.
– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
add a comment |
1
That might be a reasonable solution. But the filter functions inscipy.signalassume the data is sampled at regular intervals. If you discard thenanvalues, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.
– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
1
1
That might be a reasonable solution. But the filter functions in
scipy.signal assume the data is sampled at regular intervals. If you discard the nan values, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.– Warren Weckesser
Nov 12 at 14:00
That might be a reasonable solution. But the filter functions in
scipy.signal assume the data is sampled at regular intervals. If you discard the nan values, the sample periods of the remaining data are no longer consistent, and that may invalidate the filter results.– Warren Weckesser
Nov 12 at 14:00
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
In this case one would have to first manually resample the data at a lower frequency to match the removal of NaN values ?
– Patol75
Nov 12 at 14:39
add a comment |
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